The previous installment in this series discussed the first step in operationalizing analytics: the importance of Decision Management capabilities and collaboration between business and analytic teams for solving the right problem. This post will cover the second step in the process: creating an industrial-scale process for building analytic models.
An industrialized process incorporates five key characteristics:
- A systematic approach to data management
Organizations need to be able to use transactional data, transactional event streams, unstructured or semi-structured data and more to understand what customers are doing, for instance, or what constitutes an unacceptable risk. A systematic approach to information management that encompasses data quality, data integration and data management ensures access to data is standardized and efficient.
- A predictive analytics workbench environment
This more systematic approach to information management feeds into a workbench environment for defining the modeling flow. These workflows streamline and standardize how analytic modeling is performed. In-database mining capabilities integrated with these workflows can push data preparation, transformation and even modeling algorithms into an organization’s data infrastructure, improving throughput by reducing data movement. In-memory and other high performance analytic capabilities as well as intelligent automation of modeling activities can be applied as appropriate in the steps defined in the workflows.
- Engagement of less technical users
Historically, analytic modeling has required specialist resources. Features that allow less technical users to build and execute workflows take advantage of underlying automation capabilities to produce large numbers of “good-enough” models quickly. Working collaboratively with an analytic team, these users can produce first cut and less core analytic models, participate more fully in reviews of models and allow the analytic team to focus on high-value, high complexity problems.
- High performance analytic architecture for fast model turnaround
A high performance analytic infrastructure scales out while delivering high availability, workload management and scheduling. Products that create a distributed grid environment provide parallel job execution across multiple servers with shared physical storage. Provided algorithms have been written to take advantage of this infrastructure, dramatic performance improvements are possible.
- Ongoing management and monitoring of models
Finally, predictive analytic workbenches also support the ongoing management and monitoring of models once they are in use. Certain capabilities allow an analytic team to set up automated monitoring of models to see when they need to be re-tuned or even completely re-built. These capabilities also help track the performance of models to confirm their predictive power and behavior. This workflow too can be defined and managed, bringing even those not using the predictive analytic workbench, such as database administrators and integration specialists, into the process.
Follow this series for more about operationalizing analytics as the next post will cover implementing a reliable deployment architecture for analytic models. Or, learn more now by reading the full white paper, Operationalizing Analytics.